1,241 research outputs found
Context Trees: Augmenting Geospatial Trajectories with Context
Exposing latent knowledge in geospatial trajectories has the potential to
provide a better understanding of the movements of individuals and groups.
Motivated by such a desire, this work presents the context tree, a new
hierarchical data structure that summarises the context behind user actions in
a single model. We propose a method for context tree construction that augments
geospatial trajectories with land usage data to identify such contexts. Through
evaluation of the construction method and analysis of the properties of
generated context trees, we demonstrate the foundation for understanding and
modelling behaviour afforded. Summarising user contexts into a single data
structure gives easy access to information that would otherwise remain latent,
providing the basis for better understanding and predicting the actions and
behaviours of individuals and groups. Finally, we also present a method for
pruning context trees, for use in applications where it is desirable to reduce
the size of the tree while retaining useful information
Real-time Anomaly Detection and Localization in Crowded Scenes
In this paper, we propose a method for real-time anomaly
detection and localization in crowded scenes. Each video is
defined as a set of non-overlapping cubic patches, and is
described using two local and global descriptors. These
descriptors capture the video properties from different aspects.
By incorporating simple and cost-effective Gaussian
classifiers, we can distinguish normal activities and anomalies
in videos. The local and global features are based on
structure similarity between adjacent patches and the features
learned in an unsupervised way, using a sparse autoencoder.
Experimental results show that our algorithm is
comparable to a state-of-the-art procedure on UCSD ped2
and UMN benchmarks, but even more time-efficient. The
experiments confirm that our system can reliably detect and
localize anomalies as soon as they happen in a video
Autonomous Abnormal Behaviour Detection Using Trajectory Analysis
Abnormal behaviour detection has attracted signification amount of attention in the past decade due to increased security concerns around the world. The amount of data from surveillance cameras have exceeded human capacity and there is a greater need for anomaly detection systems for crime monitoring. This paper proposes a solution to this problem in a reception area context by using trajectory extraction through Gaussian Mixture Models and Kalman Filter for data association. Here, trajectory analysis was performed on extracted trajectories to detect four different anomalies such as entering staff area, running, loitering and squatting down. The developed anomaly detection algorithms were tested on videos captured at Asia Pacific University’s reception area. These algorithms were able to achieve a promising detection accuracy of 89% and a false positive rate of 4.52%
Hierarchical representations for spatio-temporal visual attention: modeling and understanding
Mención Internacional en el título de doctorDentro del marco de la Inteligencia Artificial, la Visión Artificial es una disciplina científica que tiene como objetivo simular automaticamente las funciones del sistema visual humano, tratando de resolver tareas como la localización y el reconocimiento de objetos, la detección de eventos o el seguimiento de objetos....Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Luis Salgado Álvarez de Sotomayor.- Secretario: Ascensión Gallardo Antolín.- Vocal: Jenny Benois Pinea
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